The impact of adoption of an electronic health record on emergency physician work: A time motion study
Bibliographic record
Abstract
Objective We assessed the impact of the transition from a primarily paper-based electronic health record (EHR) to a comprehensive EHR on emergency physician work tasks and efficiency in an academic emergency department (ED). Methods We conducted a time motion study of emergency physicians on shift in our ED. Fifteen emergency physicians were directly observed for two 4-hour sessions prior to EHR implementation, during go live, and then during post-implementation. Observers performed continuous observation and measured times for the following tasks: chart review, direct patient care, documentation, physical movement, communication, teaching, handover, and other. We compared time spent on tasks during the 3 phases of transition and analyzed mean times for the tasks per patient and per shift using 2-tailed t test for comparison. Results Physicians saw fewer patients per shift during go-live (0.51 patient/hour, P < 0.01), patient efficiency increased in post-implementation but did not recover to baseline (−0.31 patient/hour, P = 0.03). From pre-implementation to post-implementation, we observed a trend towards increased physician time spent charting (+54 seconds/patient, P = 0.05) and documenting (+36 seconds/patient, P = 0.36); time spent doing direct patient care trended towards decreasing (−0.43 seconds/patient, P = 0.23). A small percentage of shifts were spent receiving technical support and time spent on teaching activities remained relatively stable during EHR transition. Conclusion A new EHR impacts emergency physician task allocation and several changes are sustained post-implementation. Physician efficiency decreased and did not recover to baseline. Understanding workflow changes during transition to EHR in the ED is necessary to develop strategies to maintain quality of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".